You are here: Home
/ Publication Information
Title: Performance of Color Camera Machine Vision in Automated Furniture Rough Mill Systems
Author: Kline, D. Earl; Widoyoko, Agus; Wiedenbeck, Janice K.; Araman, Philip A.
Source: Forest Products Journal. 48(3): 38-45.
Publication Series: Miscellaneous Publication
Description: The objective of this study was to evaluate the performance of color camera machine vision for lumber processing in a furniture rough mill. The study used 134 red oak boards to compare the performance of automated gang-rip-first rough mill yield based on a prototype color camera lumber inspection system developed at Virginia Tech with both estimated optimum rough mill yield and actual measured rough mill yield. Automated yield was found to be 56.3 percent compared to 69.1 percent (optimum) and 65.6 percent (observed). The relatively low yield based on the color camera lumber scanning system was due to the fact that image processing algorithms were very sensitive and tended to identify and cut out defects that were not truly present. The natural variations in the color of clear wood of red oak suggests that other sensing techniques along with color sensing will be needed to accurately characterize those lumber features that are important in furniture rough mill processing.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication. (Please specify exactly
which publication you are requesting and your mailing address.)
XML: View XML
Kline, D. Earl; Widoyoko, Agus; Wiedenbeck, Janice K.; Araman, Philip A. 1998. Performance of Color Camera Machine Vision in Automated Furniture Rough Mill Systems. Forest Products Journal. 48(3): 38-45.
Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility